Depth Estimation: We firstly make comparison of depth values among our presented CI-Net and state-of-the-arts on NYU-Depth-v2 dataset where the number of categories are 40. The results are summarized in  REF . The values are used directly from the original papers or referred to the collected data of [1]}. For our network, we consider two settings of the backbone: dilated resnet-50 and dilated resnet-101, which are denoted as Res50 and Res101 respectively in the table. As it can be seen in the  REF , our model with ResNet-101 achieves best results in all metrics compared to the model using the same scale data. The rms of CI-Net is even better than those methods fed with larger training set. And the vision with dilated ResNet-50 obtains goodish values. It's also worth noting that although the methods in [2]}[3]}[4]} perform well in part of the evaluating indicators, they all attempted to feed numerous images (120k) to boost the performance and contains large amounts of parameters which hinder the inference speeds of their models. In contrast, ours has least amount of parameters and runs 1.62, 1.45, 4.53 times faster
than them respectively.
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